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added extra unit test

release/4.3a0
lcarlone 2021-04-03 17:33:12 -04:00
parent 282aa1a0a9
commit 038c1c0b8e
2 changed files with 98 additions and 1 deletions
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2
gtsam_unstable/slam/SmartStereoProjectionFactorPP.h
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@ -306,7 +306,7 @@ class SmartStereoProjectionFactorPP : public SmartStereoProjectionFactor {
case HESSIAN:
return createHessianFactor(values, lambda);
default:
throw std::runtime_error("SmartStereoFactorlinearize: unknown mode");
throw std::runtime_error("SmartStereoProjectionFactorPP: unknown linearization mode");
}
}

97
gtsam_unstable/slam/tests/testSmartStereoProjectionFactorPP.cpp
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@ -862,6 +862,103 @@ TEST( SmartStereoProjectionFactorPP, 3poses_optimization_sameExtrinsicKey ) {
EXPECT(assert_equal(expected, delta, 1e-4));
}
/* *************************************************************************/
TEST( SmartStereoProjectionFactorPP, 3poses_optimization_2ExtrinsicKeys ) {
// create first camera. Looking along X-axis, 1 meter above ground plane (x-y)
Pose3 w_Pose_cam1 = Pose3(Rot3::Ypr(-M_PI / 2, 0., -M_PI / 2), Point3(0, 0, 1));
StereoCamera cam1(w_Pose_cam1, K2);
// create second camera 1 meter to the right of first camera
Pose3 w_Pose_cam2 = w_Pose_cam1 * Pose3(Rot3(), Point3(1, 0, 0));
StereoCamera cam2(w_Pose_cam2, K2);
// create third camera 1 meter above the first camera
Pose3 w_Pose_cam3 = w_Pose_cam1 * Pose3(Rot3(), Point3(0, -1, 0));
StereoCamera cam3(w_Pose_cam3, K2);
// three landmarks ~5 meters infront of camera
Point3 landmark1(5, 0.5, 1.2);
Point3 landmark2(5, -0.5, 1.2);
Point3 landmark3(3, 0, 3.0);
// 1. Project three landmarks into three cameras and triangulate
vector<StereoPoint2> measurements_l1 = stereo_projectToMultipleCameras(cam1,
cam2, cam3, landmark1);
vector<StereoPoint2> measurements_l2 = stereo_projectToMultipleCameras(cam1,
cam2, cam3, landmark2);
vector<StereoPoint2> measurements_l3 = stereo_projectToMultipleCameras(cam1,
cam2, cam3, landmark3);
KeyVector poseKeys;
poseKeys.push_back(x1);
poseKeys.push_back(x2);
poseKeys.push_back(x3);
KeyVector extrinsicKeys;
extrinsicKeys.push_back(body_P_cam1_key);
extrinsicKeys.push_back(body_P_cam1_key);
extrinsicKeys.push_back(body_P_cam3_key);
SmartStereoProjectionParams smart_params;
smart_params.triangulation.enableEPI = true;
SmartStereoProjectionFactorPP::shared_ptr smartFactor1(new SmartStereoProjectionFactorPP(model, smart_params));
smartFactor1->add(measurements_l1, poseKeys, extrinsicKeys, K2);
SmartStereoProjectionFactorPP::shared_ptr smartFactor2(new SmartStereoProjectionFactorPP(model, smart_params));
smartFactor2->add(measurements_l2, poseKeys, extrinsicKeys, K2);
SmartStereoProjectionFactorPP::shared_ptr smartFactor3(new SmartStereoProjectionFactorPP(model, smart_params));
smartFactor3->add(measurements_l3, poseKeys, extrinsicKeys, K2);
// relevant poses:
Pose3 body_Pose_cam = Pose3(Rot3::Ypr(-M_PI, 1., 0.1),Point3(0, 1, 0));
Pose3 w_Pose_body1 = w_Pose_cam1.compose(body_Pose_cam.inverse());
Pose3 w_Pose_body2 = w_Pose_cam2.compose(body_Pose_cam.inverse());
Pose3 w_Pose_body3 = w_Pose_cam3.compose(body_Pose_cam.inverse());
// Graph
const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
NonlinearFactorGraph graph;
graph.push_back(smartFactor1);
graph.push_back(smartFactor2);
graph.push_back(smartFactor3);
graph.addPrior(x1, w_Pose_body1, noisePrior);
graph.addPrior(x2, w_Pose_body2, noisePrior);
graph.addPrior(x3, w_Pose_body3, noisePrior);
// graph.addPrior(body_P_cam1_key, body_Pose_cam, noisePrior);
// we might need some prior on the calibration too
// graph.addPrior(body_P_cam_key, body_Pose_cam, noisePrior); // no need! the measurements will fix this!
// Values
Values values;
Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100), Point3(0.01, 0.01, 0.01)); // smaller noise
values.insert(x1, w_Pose_body1);
values.insert(x2, w_Pose_body2);
values.insert(x3, w_Pose_body3);
values.insert(body_P_cam1_key, body_Pose_cam*noise_pose);
values.insert(body_P_cam3_key, body_Pose_cam*noise_pose);
// cost is large before optimization
double initialErrorSmart = graph.error(values);
EXPECT_DOUBLES_EQUAL(31986.961831653316, initialErrorSmart, 1e-5); // initial guess is noisy, so nonzero error
Values result;
gttic_(SmartStereoProjectionFactorPP);
LevenbergMarquardtOptimizer optimizer(graph, values, lm_params);
result = optimizer.optimize();
gttoc_(SmartStereoProjectionFactorPP);
tictoc_finishedIteration_();
EXPECT_DOUBLES_EQUAL(0, graph.error(result), 1e-5);
// NOTE: the following would fail since the problem is underconstrained (while LM above works just fine!)
// GaussianFactorGraph::shared_ptr GFG = graph.linearize(result);
// VectorValues delta = GFG->optimize();
// VectorValues expected = VectorValues::Zero(delta);
// EXPECT(assert_equal(expected, delta, 1e-4));
}
/* *************************************************************************/
TEST( SmartStereoProjectionFactorPP, monocular_multipleExtrinsicKeys ){
// make a realistic calibration matrix